A data collection and analysis engine built into an application installed on a client device may be employed to collect, analyze, and report application data desired from the client device to a service provider associated with the application. However, data collection and analysis performed by a conventional data collection and analysis engine may be limited to parameters set by a developer. Often the parameters may accommodate for low-end devices that have limited hardware resources to ensure that the data collection and analysis performed by the data collection and analysis engine only takes a small footprint on the device, and does not cause software performance issues.
In some scenarios, it may be desirable for the data collection and analysis engine to collect and analyze data based on parameters corresponding to hardware resources available for a particular, target device on which the application is being executed. However, with the sheer number of device types employed today, the time and cost to develop and manage parameters for data collection and analysis such that the parameters correspond to the hardware resources for each device type would be excessive. Accordingly, conventional methods and engines for data collection and analysis could use improvements to enable dynamic variance of the parameters of the data collection and analysis such that the parameters may correspond to the hardware resources for any device type.